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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from pyquaternion import Quaternion
from nuscenes.prediction import PredictHelper, convert_local_coords_to_global
from tools.analysis_tools.visualize.render.base_render import BaseRender
from tools.analysis_tools.visualize.utils import color_mapping, AgentPredictionData
class BEVRender(BaseRender):
"""
Render class for BEV
"""
def __init__(self,
figsize=(20, 20),
margin: float = 50,
view: np.ndarray = np.eye(4),
show_gt_boxes=False):
super(BEVRender, self).__init__(figsize)
self.margin = margin
self.view = view
self.show_gt_boxes = show_gt_boxes
def set_plot_cfg(self):
self.axes.set_xlim([-self.margin, self.margin])
self.axes.set_ylim([-self.margin, self.margin])
self.axes.set_aspect('equal')
self.axes.grid(False)
def render_sample_data(self, canvas, sample_token):
pass
def render_anno_data(
self,
sample_token,
nusc,
predict_helper):
sample_record = nusc.get('sample', sample_token)
assert 'LIDAR_TOP' in sample_record['data'].keys(
), 'Error: No LIDAR_TOP in data, unable to render.'
lidar_record = sample_record['data']['LIDAR_TOP']
data_path, boxes, _ = nusc.get_sample_data(
lidar_record, selected_anntokens=sample_record['anns'])
for box in boxes:
instance_token = nusc.get('sample_annotation', box.token)[
'instance_token']
future_xy_local = predict_helper.get_future_for_agent(
instance_token, sample_token, seconds=6, in_agent_frame=True)
if future_xy_local.shape[0] > 0:
trans = box.center
rot = Quaternion(matrix=box.rotation_matrix)
future_xy = convert_local_coords_to_global(
future_xy_local, trans, rot)
future_xy = np.concatenate(
[trans[None, :2], future_xy], axis=0)
c = np.array([0, 0.8, 0])
box.render(self.axes, view=self.view, colors=(c, c, c))
self._render_traj(future_xy, line_color=c, dot_color=(0, 0, 0))
self.axes.set_xlim([-self.margin, self.margin])
self.axes.set_ylim([-self.margin, self.margin])
def show_lidar_data(
self,
sample_token,
nusc):
sample_record = nusc.get('sample', sample_token)
assert 'LIDAR_TOP' in sample_record['data'].keys(
), 'Error: No LIDAR_TOP in data, unable to render.'
lidar_record = sample_record['data']['LIDAR_TOP']
data_path, boxes, _ = nusc.get_sample_data(
lidar_record, selected_anntokens=sample_record['anns'])
LidarPointCloud.from_file(data_path).render_height(
self.axes, view=self.view)
self.axes.set_xlim([-self.margin, self.margin])
self.axes.set_ylim([-self.margin, self.margin])
self.axes.axis('off')
self.axes.set_aspect('equal')
def render_pred_box_data(self, agent_prediction_list):
for pred_agent in agent_prediction_list:
c = np.array([0, 1, 0])
if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true
tr_id = pred_agent.pred_track_id
c = color_mapping[tr_id % len(color_mapping)]
pred_agent.nusc_box.render(
axis=self.axes, view=self.view, colors=(c, c, c))
if pred_agent.is_sdc:
c = np.array([1, 0, 0])
pred_agent.nusc_box.render(
axis=self.axes, view=self.view, colors=(c, c, c))
def render_pred_traj(self, agent_prediction_list, top_k=3):
for pred_agent in agent_prediction_list:
if pred_agent.is_sdc:
continue
sorted_ind = np.argsort(pred_agent.pred_traj_score)[
::-1] # from high to low
num_modes = len(sorted_ind)
sorted_traj = pred_agent.pred_traj[sorted_ind, :, :2]
sorted_score = pred_agent.pred_traj_score[sorted_ind]
# norm_score = np.sum(np.exp(sorted_score))
norm_score = np.exp(sorted_score[0])
sorted_traj = np.concatenate(
[np.zeros((num_modes, 1, 2)), sorted_traj], axis=1)
trans = pred_agent.pred_center
rot = Quaternion(axis=np.array([0, 0.0, 1.0]), angle=np.pi/2)
vehicle_id_list = [0, 1, 2, 3, 4, 6, 7]
if pred_agent.pred_label in vehicle_id_list:
dot_size = 150
else:
dot_size = 25
# print(sorted_score)
for i in range(top_k-1, -1, -1):
viz_traj = sorted_traj[i, :, :2]
viz_traj = convert_local_coords_to_global(viz_traj, trans, rot)
traj_score = np.exp(sorted_score[i])/norm_score
# traj_score = [1.0, 0.01, 0.01, 0.01, 0.01, 0.01][i]
self._render_traj(viz_traj, traj_score=traj_score,
colormap='winter', dot_size=dot_size)
def render_pred_map_data(self, predicted_map_seg):
# rendered_map = map_color_dict
# divider, crossing, contour
map_color_dict = np.array(
[(204, 128, 0), (102, 255, 102), (102, 255, 102)])
rendered_map = map_color_dict[predicted_map_seg.argmax(
-1).reshape(-1)].reshape(200, 200, -1)
bg_mask = predicted_map_seg.sum(-1) == 0
rendered_map[bg_mask, :] = 255
self.axes.imshow(rendered_map, alpha=0.6,
interpolation='nearest', extent=(-51.2, 51.2, -51.2, 51.2))
def render_occ_map_data(self, agent_list):
rendered_map = np.ones((200, 200, 3))
rendered_map_hsv = matplotlib.colors.rgb_to_hsv(rendered_map)
occ_prob_map = np.zeros((200, 200))
for i in range(len(agent_list)):
pred_agent = agent_list[i]
if pred_agent.pred_occ_map is None:
continue
if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true
tr_id = pred_agent.pred_track_id
c = color_mapping[tr_id % len(color_mapping)]
pred_occ_map = pred_agent.pred_occ_map.max(0)
update_mask = pred_occ_map > occ_prob_map
occ_prob_map[update_mask] = pred_occ_map[update_mask]
pred_occ_map *= update_mask
hsv_c = matplotlib.colors.rgb_to_hsv(c)
rendered_map_hsv[pred_occ_map > 0.1] = (
np.ones((200, 200, 1)) * hsv_c)[pred_occ_map > 0.1]
max_prob = pred_occ_map.max()
renorm_pred_occ_map = (pred_occ_map - max_prob) * 0.7 + 1
sat_map = (renorm_pred_occ_map * hsv_c[1])
rendered_map_hsv[pred_occ_map > 0.1,
1] = sat_map[pred_occ_map > 0.1]
rendered_map = matplotlib.colors.hsv_to_rgb(rendered_map_hsv)
self.axes.imshow(rendered_map, alpha=0.8,
interpolation='nearest', extent=(-50, 50, -50, 50))
def render_occ_map_data_time(self, agent_list, t):
rendered_map = np.ones((200, 200, 3))
rendered_map_hsv = matplotlib.colors.rgb_to_hsv(rendered_map)
occ_prob_map = np.zeros((200, 200))
for i in range(len(agent_list)):
pred_agent = agent_list[i]
if pred_agent.pred_occ_map is None:
continue
if hasattr(pred_agent, 'pred_track_id') and pred_agent.pred_track_id is not None: # this is true
tr_id = pred_agent.pred_track_id
c = color_mapping[tr_id % len(color_mapping)]
pred_occ_map = pred_agent.pred_occ_map[t]
update_mask = pred_occ_map > occ_prob_map
occ_prob_map[update_mask] = pred_occ_map[update_mask]
pred_occ_map *= update_mask
hsv_c = matplotlib.colors.rgb_to_hsv(c)
rendered_map_hsv[pred_occ_map > 0.1] = (
np.ones((200, 200, 1)) * hsv_c)[pred_occ_map > 0.1]
max_prob = pred_occ_map.max()
renorm_pred_occ_map = (pred_occ_map - max_prob) * 0.7 + 1
sat_map = (renorm_pred_occ_map * hsv_c[1])
rendered_map_hsv[pred_occ_map > 0.1,
1] = sat_map[pred_occ_map > 0.1]
rendered_map = matplotlib.colors.hsv_to_rgb(rendered_map_hsv)
self.axes.imshow(rendered_map, alpha=0.8,
interpolation='nearest', extent=(-50, 50, -50, 50))
def render_planning_data(self, predicted_planning, show_command=False):
planning_traj = predicted_planning.pred_traj
planning_traj = np.concatenate(
[np.zeros((1, 2)), planning_traj], axis=0)
self._render_traj(planning_traj, colormap='autumn', dot_size=50)
if show_command:
self._render_command(predicted_planning.command)
def render_planning_attn_mask(self, predicted_planning):
planning_attn_mask = predicted_planning.attn_mask
planning_attn_mask = planning_attn_mask/planning_attn_mask.max()
cmap_name = 'plasma'
self.axes.imshow(planning_attn_mask, alpha=0.8, interpolation='nearest', extent=(
-51.2, 51.2, -51.2, 51.2), vmax=0.2, cmap=matplotlib.colormaps[cmap_name])
def render_hd_map(self, nusc, nusc_maps, sample_token):
sample_record = nusc.get('sample', sample_token)
sd_rec = nusc.get('sample_data', sample_record['data']['LIDAR_TOP'])
cs_record = nusc.get('calibrated_sensor',
sd_rec['calibrated_sensor_token'])
pose_record = nusc.get('ego_pose', sd_rec['ego_pose_token'])
info = {
'lidar2ego_translation': cs_record['translation'],
'lidar2ego_rotation': cs_record['rotation'],
'ego2global_translation': pose_record['translation'],
'ego2global_rotation': pose_record['rotation'],
'scene_token': sample_record['scene_token']
}
layer_names = ['road_divider', 'road_segment', 'lane_divider',
'lane', 'road_divider', 'traffic_light', 'ped_crossing']
map_mask = obtain_map_info(nusc,
nusc_maps,
info,
patch_size=(102.4, 102.4),
canvas_size=(1024, 1024),
layer_names=layer_names)
map_mask = np.flip(map_mask, axis=1)
map_mask = np.rot90(map_mask, k=-1, axes=(1, 2))
map_mask = map_mask[:, ::-1] > 0
map_show = np.ones((1024, 1024, 3))
map_show[map_mask[0], :] = np.array([1.00, 0.50, 0.31])
map_show[map_mask[1], :] = np.array([159./255., 0.0, 1.0])
self.axes.imshow(map_show, alpha=0.2, interpolation='nearest',
extent=(-51.2, 51.2, -51.2, 51.2))
def _render_traj(self, future_traj, traj_score=1, colormap='winter', points_per_step=20, line_color=None, dot_color=None, dot_size=25):
total_steps = (len(future_traj)-1) * points_per_step + 1
dot_colors = matplotlib.colormaps[colormap](
np.linspace(0, 1, total_steps))[:, :3]
dot_colors = dot_colors*traj_score + \
(1-traj_score)*np.ones_like(dot_colors)
total_xy = np.zeros((total_steps, 2))
for i in range(total_steps-1):
unit_vec = future_traj[i//points_per_step +
1] - future_traj[i//points_per_step]
total_xy[i] = (i/points_per_step - i//points_per_step) * \
unit_vec + future_traj[i//points_per_step]
total_xy[-1] = future_traj[-1]
self.axes.scatter(
total_xy[:, 0], total_xy[:, 1], c=dot_colors, s=dot_size)
def _render_command(self, command):
command_dict = ['TURN RIGHT', 'TURN LEFT', 'KEEP FORWARD']
self.axes.text(-48, -45, command_dict[int(command)], fontsize=45)
def render_sdc_car(self):
sdc_car_png = cv2.imread('sources/sdc_car.png')
sdc_car_png = cv2.cvtColor(sdc_car_png, cv2.COLOR_BGR2RGB)
self.axes.imshow(sdc_car_png, extent=(-1, 1, -2, 2))
def render_legend(self):
legend = cv2.imread('sources/legend.png')
legend = cv2.cvtColor(legend, cv2.COLOR_BGR2RGB)
self.axes.imshow(legend, extent=(23, 51.2, -50, -40))
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